SceneDreamer
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About
SceneDreamer is an unconditional generative model designed for creating unbounded 3D scenes, synthesizing large-scale 3D landscapes from random noises. This framework learns exclusively from in-the-wild 2D image collections without requiring any 3D annotations. Its core paradigm includes an efficient bird's-eye-view (BEV) representation, which consists of a height field for surface elevation and a semantic field for scene details. This BEV representation offers quadratic complexity for 3D scene representation, disentangled geometry and semantics, and efficient training. The tool also employs a novel generative neural hash grid to parameterize the latent space using 3D positions and scene semantics, encoding generalizable features. Finally, a neural volumetric renderer, trained adversarially on 2D images, produces photorealistic results. SceneDreamer excels in generating vivid and diverse unbounded 3D worlds.
Platform
Task
Features
• produces photorealistic images via a neural volumetric renderer
• employs a novel generative neural hash grid
• utilizes an efficient bird's-eye-view (bev) scene representation
• allows free camera trajectory
• ensures 3d consistency and well-defined depth
• synthesizes diverse landscapes across different styles
• learns from in-the-wild 2d image collections
• unbounded 3d scene generation
Pricing Plans
Free
Free Plan• Generate unbounded 3D scenes
• Utilize in-the-wild 2D image collections
• Efficient BEV representation
• Photorealistic rendering
• Code available on GitHub
• Demo available on Hugging Face Spaces
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